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Feedforward control based on orthogonal least square for a charge-coupled device-based tracking loop

机译:基于正交最小二乘的前馈控制基于电荷耦合器件的跟踪环路

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A prediction feedforward control based data fusion is proposed to enhance closed-loop performance in electro-optical tracking system. The aim of feedforward controller is to estimate the target trajectory and velocity by synthesizing line-of-sight error and angular position from the encoder. However, time delay existing in every data fusion algorithm will still affect the tracking performance. In this paper, an orthogonal least square (OLS) prediction method is proposed to improve the tracking precision. The transfer function of OLS in frequency domain is built to analyze the time-delay characteristics, which indicates a less time-delay and higher bandwidth than classical Kalman filters. In the experiments, 50 sampled data and second order polynomial are used in OLS to tracking unmanned aerial vehicle (UAV) by electro-optical tracking system. Some comparative experiments compared with Kalman filter show that the prediction feedforward control based on OLS has better performance for reducing the tracking error.
机译:提出了一种基于预测前馈控制的数据融合技术,以提高电光跟踪系统的闭环性能。前馈控制器的目的是通过合成来自编码器的视线误差和角位置来估计目标轨迹和速度。但是,每种数据融合算法中存在的时间延迟仍然会影响跟踪性能。为了提高跟踪精度,提出了一种正交最小二乘(OLS)预测方法。建立OLS的频域传递函数来分析时延特性,这表明与传统的卡尔曼滤波器相比,时延更短且带宽更高。在实验中,在OLS中使用了50个采样数据和二阶多项式,通过电光跟踪系统跟踪无人机。与Kalman滤波器进行的一些比较实验表明,基于OLS的预测前馈控制在降低跟踪误差方面具有更好的性能。

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